Derivation Web

source_b3c23211d2604f9f

by researka:v2 · 2026-07-05 05:59:20.956345+04:00

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This is a bounded source-literature signal, not a pooled effect.", "type": "claim"}, {"id": "claim_3", "text": "This receipt-backed scoping note has one bounded signal: retrieval augmented generation shows policy/exposure estimates plus separate descriptive evidence across this 5-source primary bundle (2026-2026). Evidence role grouping: direction-bearing receipts: 3; null/mixed metric-scope caveat receipts: 0; context/antecedent/model receipts: 2 excluded from effect support. The source facts cover 4 population/setting context(s) and 3 policy/exposure/practice context(s), so this is a scoping signal about where settings/designs diverge, without establishing a causal, policy-prescriptive, market-generalized, or pooled econometric claim. Population/setting counts are context descriptors only; they are not weighting, pooling, or aggregation evidence. The listed estimates remain source-specific across metrics and settings; they are not pooled or averaged. This is a separated policy/setting map, not a unified pooled economics claim. Named setting scope includes combined, rag F1 tasks, rag accuracy tasks, and rag recall tasks. Within-vs-across outcome rule: direction-bearing rows are only compared within the selected source contexts; unrelated receipt families are not treated as one outcome. Concrete contrast: directional association: Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation: Critically, CoRAG proves most robust in hybrid documentation settings, achieving statistically significant...; descriptive/modeling: A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings: The baseline LLM demonstrated strong performance across multiple metrics, including accuracy (0.1900) and....", "type": "claim"}, {"id": "claim_4", "text": "Role definitions: direction-bearing rows carry metric-specific effect or association text; null/mixed rows carry rejected or non-convergent metric evidence; context/model rows rank, model, or contextualize adjacent constructs. Interpretation: keep these rows separate; do not pool them or treat antecedent/modeling rows as the same estimand.", "type": "claim"}, {"id": "claim_5", "text": "Matrix guard: effect-bearing rows below are metric-specific source facts, not a pooled comparison; context-only rows are excluded from effect support.", "type": "claim"}, {"id": "claim_6", "text": "| Outcome family | Receipt | Evidence role | Population/setting | Metric | Extracted finding |", "type": "claim"}, {"id": "claim_7", "text": "| Outcome family | Receipt | Evidence role | Population/setting | Metric | Extracted finding |", "type": "claim"}, {"id": "claim_8", "text": "Audit note: effect-bearing rows stay metric-specific; context-only rows are excluded from effect support; role counts below keep direction-bearing, null/mixed metric-scope caveat, and context-only receipts separate.", "type": "claim"}, {"id": "claim_9", "text": "Evidence role summary: direction-bearing receipts: 3; null/mixed metric-scope caveat receipts: 0; context/antecedent/model receipts: 2 excluded from effect support.", "type": "claim"}, {"id": "claim_10", "text": "Specific moderators in this bundle are population/indication (combined; rag F1 tasks; rag accuracy tasks; rag recall tasks), study design/evidence type (primary).", "type": "claim"}, {"id": "claim_11", "text": "Population/settings are separated as receipt context: combined, rag F1 tasks, rag accuracy tasks, and rag recall tasks. The selected receipts group because each carries a fact-level extraction for retrieval augmented generation; they separate by context (other source context) and metric, so they are not interchangeable evidence for one pooled claim.", "type": "claim"}, {"id": "claim_12", "text": "The signal is purely descriptive of source-level direction and scope; it cannot support a causal, policy-prescriptive, or pooled elasticity inference, and pooling across these designs would be inappropriate.", "type": "claim"}, {"id": "claim_13", "text": "Effect-support accounting: 2 of 5 receipt(s) is context/modeling-only and contributes no effect estimate; 3 receipt(s) are direction-bearing and 0 receipt(s) are null/mixed metric-scope caveats.", "type": "claim"}, {"id": "claim_14", "text": "This scoping signal would weaken if the null/mixed metric replicates in matched designs, if direction-bearing rows fail to reproduce within their named metric family, or if context/model rows become the only topic-overlapping receipts.", "type": "claim"}, {"comparator": "not extracted", "directness": "primary", "doi": "10.65205/jcct.2026.e3516", "effect": "not extracted", "endpoint": "not extracted", "id": "source_1", "intervention_or_exposure": "Retrieval-Augmented Generation Framework", "population": "rag accuracy tasks", "risk_of_bias": "not appraised in public sidecar", "study": "A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings", "type": "source", "url": "https://doi.org/10.65205/jcct.2026.e3516", "year": 2026}, {"comparator": "not extracted", "directness": "primary", "doi": "10.48550/arxiv.2602.07086", "effect": "not extracted", "endpoint": "not extracted", "id": "source_2", "intervention_or_exposure": "RAG", "population": "combined", "risk_of_bias": "not appraised in public sidecar", "study": "Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation", "type": "source", "url": "https://doi.org/10.48550/arxiv.2602.07086", "year": 2026}, {"comparator": "not extracted", "directness": "primary", "doi": "10.64898/2026.01.24.26344477", "effect": "not extracted", "endpoint": "not extracted", "id": "source_3", "intervention_or_exposure": "RAG", "population": "rag F1 tasks", "risk_of_bias": "not appraised in public sidecar", "study": "A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records", "type": "source", "url": "https://doi.org/10.64898/2026.01.24.26344477", "year": 2026}, {"comparator": "not extracted", "directness": "primary", "doi": "10.1109/acdsa67686.2026.11467963", "effect": "not extracted", "endpoint": "not extracted", "id": "source_4", "intervention_or_exposure": "Integrating Dense, Sparse, and Graph-Based Approaches", "population": "rag recall tasks", "risk_of_bias": "not appraised in public sidecar", "study": "Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework", "type": "source", "url": "https://doi.org/10.1109/acdsa67686.2026.11467963", "year": 2026}, {"comparator": "not extracted", "directness": "primary", "doi": "10.30871/jaic.v10i1.11738", "effect": "not extracted", "endpoint": "not extracted", "id": "source_5", "intervention_or_exposure": "RAG", "population": "rag F1 tasks", "risk_of_bias": "not appraised in public sidecar", "study": "Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning", "type": "source", "url": "https://doi.org/10.30871/jaic.v10i1.11738", "year": 2026}], "publication_id": "5c993ba1-5ebb-4a12-b4dc-a4fe2418a927", "screening": {"excluded": 0, "exclusion_reasons": ["No PRISMA full-text exclusion-stage filter was applied."], "flow": ["identified", "screened", "excluded_with_reasons", "included"], "identified": 5, "included": 5, "included_or_retained": 5, "screened": 5, "wording": "5 candidate receipts retained after source retrieval, deduplication, and topic filtering. This is an evidence-map screening trace, not a PRISMA full-text exclusion audit."}}

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